Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling

• URL: http://arxiv.org/abs/2402.17622v1
• Date: Tue, 27 Feb 2024 15:49:54 GMT
• Title: Masked Gamma-SSL: Learning Uncertainty Estimation via Masked Image Modeling
• Authors: David S. W. Williams, Matthew Gadd, Paul Newman and Daniele De Martini
• Abstract summary: This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass. We exploit general representations from foundation models and unlabelled datasets through a Masked Image Modeling (MIM) approach. For neural networks used in safety-critical applications, bias in the training data can lead to errors.
• Score: 19.000718685399935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass. We exploit general representations from foundation models and unlabelled datasets through a Masked Image Modeling (MIM) approach, which is robust to augmentation hyper-parameters and simpler than previous techniques. For neural networks used in safety-critical applications, bias in the training data can lead to errors; therefore it is crucial to understand a network's limitations at run time and act accordingly. To this end, we test our proposed method on a number of test domains including the SAX Segmentation benchmark, which includes labelled test data from dense urban, rural and off-road driving domains. The proposed method consistently outperforms uncertainty estimation and Out-of-Distribution (OoD) techniques on this difficult benchmark.

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